JECL is also robust to incomplete multi-view tasks, which have received increasing attention in recent years. DAIMC [1]} is a method built on weighted semi-nonnegative matrix factorization. It learns a shared feature matrix for all views and prevents the effect from missing view with \(L_{2,1}\) -Norm regularized regression. Wang et al. [2]} build a bridge between perturbation risk bounds and missing view problems and propose PIC which reduces perturbation risk among all views and learns a consensus Laplacian matrix.
